In [ ]:
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Load the data
file_path = '/Users/lakshay/Desktop/graucia/loblaws.xlsx'
df = pd.read_excel(file_path, sheet_name='E Comm') # Load the E Comm sheet
In [ ]:
# Display the first few rows of the dataframe to understand its structure
print(df.head())
print(df.columns)
# Data Cleaning
# Handle missing values
df.fillna(df.median(numeric_only=True), inplace=True) # Fill numerical NaNs with median
df.fillna(df.mode().iloc[0], inplace=True) # Fill categorical NaNs with mode
CustomerID Churn Tenure PreferredLoginDevice CityTier WarehouseToHome \
0 50001 1 4.0 Mobile Phone 3 6.0
1 50002 1 NaN Phone 1 8.0
2 50003 1 NaN Phone 1 30.0
3 50004 1 0.0 Phone 3 15.0
4 50005 1 0.0 Phone 1 12.0
PreferredPaymentMode Gender HourSpendOnApp NumberOfDeviceRegistered \
0 Debit Card Female 3.0 3
1 UPI Male 3.0 4
2 Debit Card Male 2.0 4
3 Debit Card Male 2.0 4
4 CC Male NaN 3
PreferedOrderCat SatisfactionScore MaritalStatus NumberOfAddress \
0 Laptop & Accessory 2 Single 9
1 Mobile 3 Single 7
2 Mobile 3 Single 6
3 Laptop & Accessory 5 Single 8
4 Mobile 5 Single 3
Complain OrderAmountHikeFromlastYear CouponUsed OrderCount \
0 1 11.0 1.0 1.0
1 1 15.0 0.0 1.0
2 1 14.0 0.0 1.0
3 0 23.0 0.0 1.0
4 0 11.0 1.0 1.0
DaySinceLastOrder CashbackAmount
0 5.0 159.93
1 0.0 120.90
2 3.0 120.28
3 3.0 134.07
4 3.0 129.60
Index(['CustomerID', 'Churn', 'Tenure', 'PreferredLoginDevice', 'CityTier',
'WarehouseToHome', 'PreferredPaymentMode', 'Gender', 'HourSpendOnApp',
'NumberOfDeviceRegistered', 'PreferedOrderCat', 'SatisfactionScore',
'MaritalStatus', 'NumberOfAddress', 'Complain',
'OrderAmountHikeFromlastYear', 'CouponUsed', 'OrderCount',
'DaySinceLastOrder', 'CashbackAmount'],
dtype='object')
In [ ]:
# Ensure Churn is treated as a categorical variable
df['Churn'] = df['Churn'].astype(int)
# Identify categorical columns
categorical_columns = df.select_dtypes(include=['object']).columns
print("Categorical columns:", categorical_columns)
# Encode categorical columns
from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
for col in categorical_columns:
df[col] = label_encoder.fit_transform(df[col])
# Identify numerical columns
numerical_columns = df.select_dtypes(include=[np.number]).columns
print("Numerical columns:", numerical_columns)
Categorical columns: Index(['PreferredLoginDevice', 'PreferredPaymentMode', 'Gender',
'PreferedOrderCat', 'MaritalStatus'],
dtype='object')
Numerical columns: Index(['CustomerID', 'Churn', 'Tenure', 'PreferredLoginDevice', 'CityTier',
'WarehouseToHome', 'PreferredPaymentMode', 'Gender', 'HourSpendOnApp',
'NumberOfDeviceRegistered', 'PreferedOrderCat', 'SatisfactionScore',
'MaritalStatus', 'NumberOfAddress', 'Complain',
'OrderAmountHikeFromlastYear', 'CouponUsed', 'OrderCount',
'DaySinceLastOrder', 'CashbackAmount'],
dtype='object')
In [ ]:
# Descriptive Statistics
print(df.describe())
# Churn Distribution
plt.figure(figsize=(8, 6))
sns.countplot(x='Churn', data=df)
plt.title('Churn Distribution')
plt.show()
# Feature Distributions with respect to Churn
for column in df.columns:
if column != 'Churn':
plt.figure(figsize=(8, 6))
sns.histplot(data=df, x=column, hue='Churn', multiple='stack', kde=True)
plt.title(f'Distribution of {column} with respect to Churn')
plt.show()
CustomerID Churn Tenure PreferredLoginDevice \
count 5630.000000 5630.000000 5630.000000 5630.000000
mean 52815.500000 0.168384 10.134103 0.928419
std 1625.385339 0.374240 8.357951 0.709822
min 50001.000000 0.000000 0.000000 0.000000
25% 51408.250000 0.000000 3.000000 0.000000
50% 52815.500000 0.000000 9.000000 1.000000
75% 54222.750000 0.000000 15.000000 1.000000
max 55630.000000 1.000000 61.000000 2.000000
CityTier WarehouseToHome PreferredPaymentMode Gender \
count 5630.000000 5630.000000 5630.000000 5630.000000
mean 1.654707 15.566785 3.548135 0.601066
std 0.915389 8.345961 1.389659 0.489723
min 1.000000 5.000000 0.000000 0.000000
25% 1.000000 9.000000 3.000000 0.000000
50% 1.000000 14.000000 4.000000 1.000000
75% 3.000000 20.000000 4.000000 1.000000
max 3.000000 127.000000 6.000000 1.000000
HourSpendOnApp NumberOfDeviceRegistered PreferedOrderCat \
count 5630.000000 5630.000000 5630.000000
mean 2.934636 3.688988 2.369627
std 0.705528 1.023999 1.411435
min 0.000000 1.000000 0.000000
25% 2.000000 3.000000 2.000000
50% 3.000000 4.000000 2.000000
75% 3.000000 4.000000 4.000000
max 5.000000 6.000000 5.000000
SatisfactionScore MaritalStatus NumberOfAddress Complain \
count 5630.000000 5630.000000 5630.000000 5630.000000
mean 3.066785 1.168384 4.214032 0.284902
std 1.380194 0.664344 2.583586 0.451408
min 1.000000 0.000000 1.000000 0.000000
25% 2.000000 1.000000 2.000000 0.000000
50% 3.000000 1.000000 3.000000 0.000000
75% 4.000000 2.000000 6.000000 1.000000
max 5.000000 2.000000 22.000000 1.000000
OrderAmountHikeFromlastYear CouponUsed OrderCount \
count 5630.000000 5630.000000 5630.000000
mean 15.674600 1.716874 2.961812
std 3.591058 1.857640 2.879248
min 11.000000 0.000000 1.000000
25% 13.000000 1.000000 1.000000
50% 15.000000 1.000000 2.000000
75% 18.000000 2.000000 3.000000
max 26.000000 16.000000 16.000000
DaySinceLastOrder CashbackAmount
count 5630.000000 5630.000000
mean 4.459325 177.223030
std 3.570626 49.207036
min 0.000000 0.000000
25% 2.000000 145.770000
50% 3.000000 163.280000
75% 7.000000 196.392500
max 46.000000 324.990000
In [ ]:
# Correlation Matrix
plt.figure(figsize=(14, 12))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.show()
# Pair Plots
sns.pairplot(df, hue='Churn', diag_kind='kde')
plt.show()
In [ ]:
# Further EDA based on feature importance
import shap
# Data Splitting
X = df.drop('Churn', axis=1)
y = df['Churn']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Model Training
from sklearn.ensemble import RandomForestClassifier
best_model = RandomForestClassifier()
best_model.fit(X_train, y_train)
Out[Â ]:
RandomForestClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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RandomForestClassifier()
In [ ]:
# SHAP for Model Interpretation
explainer = shap.TreeExplainer(best_model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)
# Feature Importance from Random Forest
importances = best_model.feature_importances_
indices = np.argsort(importances)[::-1]
# Plot the feature importances
plt.figure(figsize=(12, 6))
plt.title("Feature Importances")
plt.bar(range(X_train.shape[1]), importances[indices], align="center")
plt.xticks(range(X_train.shape[1]), X_train.columns[indices], rotation=90)
plt.xlim([-1, X_train.shape[1]])
plt.show()
